Generative AI Threat Mapping for Real-Time Data Vulnerability Detection

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Solution Overview

Problem

In large network environments, it is challenging to ensure data security by identifying and visualizing data security vulnerabilities in real time, especially with numerous remote data transmissions, due to the vast amount of data and the difficulty in predicting threat actor behaviors and visualizing global threats effectively.

Innovation Solution

A system utilizing generative AI to generate data security models and visualizations by collecting historical data, determining geographic location identifiers, and generating record snapshots and geographic maps to predict potential threats and vulnerabilities in real time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Difficulty of detecting and measuring

If traditional data security analysis methods are used to examine each data transmission, then security detection capability is improved, but processing speed and real-time analysis capability deteriorate due to the vast amount of data

Engineering Contradiction:

Improvedata security vulnerability detection capabilityVSAvoiddata transmission processing speed

Core Design Contradiction:

Difficulty of detecting and measuringVSProductivity

Solution Approach 1:

The patent segments the large volume of data transmission into individual units, each analyzed by dedicated AI models. The system divides data streams into discrete packets and processes them through specialized security analysis modules, enabling parallel processing that maintains both detection depth and processing speed

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent replaces traditional mechanical rule-based security analysis with generative AI models that can automatically learn and adapt security patterns. The AI engine substitutes manual security rule configuration and analysis with automated machine learning models that process data transmissions dynamically, improving both detection accuracy and processing efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive analysis of all data transmissions is performed, then security coverage is improved, but system complexity and computational resources required deteriorate

Engineering Contradiction:

Improvesecurity coverageVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a universal generative AI engine that performs multiple security functions simultaneously - threat detection, pattern recognition, anomaly identification, and risk assessment. This multi-functional system consolidates what would otherwise require separate specialized tools, reducing overall system complexity while maintaining comprehensive security coverage

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent dynamically adjusts analysis parameters such as detection sensitivity thresholds, scanning depth, and resource allocation based on real-time conditions. The system modifies its operational parameters to balance security coverage with computational efficiency, reducing complexity during low-risk periods while maintaining high coverage when threats are detected

Inventive Principle:

Principle #35Parameter changes

3Loss of time

If real-time analysis of data transmissions is implemented, then threat response time is improved, but computational power and processing capacity required deteriorate

Engineering Contradiction:

Improvethreat response timeVSAvoidcomputational power consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The patent implements periodic analysis cycles where data transmissions are examined at strategically determined intervals rather than continuously. The system uses time-based sampling and batch processing techniques that provide timely threat detection while reducing computational power consumption by eliminating redundant continuous analysis of benign traffic patterns

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The generative AI models perform self-optimization by automatically adjusting their computational resource usage based on detected threat patterns. The system allocates processing power dynamically, intensifying analysis only when anomalies are detected and reducing computational load during normal operation, thereby maintaining fast response times while managing power consumption efficiently

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260017288A1Systems and methods for dynamically generating data security models and visualizations of data security vulnerabilities using generative artificial intelligence

Publication Date: 2026.01.15 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for dynamically generating data security models and visualizations of data security vulnerabilities using generative artificial intelligence. The present invention is configured to collect historical data associated with at least one data transmission; determine at least one geographic location identifier for the at least one data transmission; determine a user identifier for the at least one data transmission; generate, by a generative artificial intelligence (AI) engine, a record snapshot of the at least one data transmission and the at least one geographic location identifier, wherein the record snapshot comprises at least one context dataset generated by the generative AI engine; and generate, by the generative AI engine, a geographic map comprising at least one data point for the historical data and the at least one geographic location identifier.